Precise Biometrics Ab

Secumanagement B.v.

Fredrik Rosqvist

Anders Fredriksson

A method for authenticating an identity of a person may involve acquiring a sample fingerprint image of a finger of the person. Two or more sub-portions may be extracted from the sample fingerprint image to form a plurality of fingerprint test images. A fingerprint recognition may be independently run on the plurality of fingerprint test images. The identity of the person may be authenticated upon a positive outcome from the independently run finger-print recognitions. A device may be provided for authenticating an identity of a person.

Method and device for authenticating an identity of a person

A method for updating biometric reference data for a person is provided. The biometric reference data is defined as a multi-template formed by a number of templates each representing an area of an associated biometric object of the person. The method comprises providing at least one template associated with a biometric image that depicts at least one area of a finger of the person. Moreover, the method comprises matching the at least one template to the multi-template of the biometric reference data and in the event of a match, updating the multi-template of the biometric reference data with at least one selected previously matched template stored in a candidate template queue. Furthermore, the method comprises storing the matched template in the candidate template queue.

Method for updating biometric reference data

A method of matching fingerprints is disclosed. The method comprises, for a first minutia point, being assigned as a planet minutia point a) determining pairs including the planet minutia point and a satellite minutia point, respectively, such that a cluster is formed; b) comparing the clusters of the respective sets and excluding nonmatching satellites; c) counting links in the cluster formed by remaining pairs; and d) for remaining satellite minutia points, performing steps a) to c) with respective satellite minutia point assigned as planet minutia point to form a supercluster by iterating steps a) to d) and superadding the clusters; calculating a score of the supercluster based on the aggregate counted links; and comparing the score with a threshold. A biometric matching apparatus, a portable data carrier, a data processing unit comprising a matching apparatus and a computer program for implementing the invention are also disclosed.

Method of matching, biometric matching apparatus, and computer program

A method of matching fingerprints is disclosed. The method comprises, for a first minutia point, being assigned as a planet minutia point a) determining pairs including the planet minutia point and a satellite minutia point, respectively, such that a cluster is formed; b) comparing the clusters of the respective sets and excluding non-matching satellites; c) counting links in the cluster formed by remaining pairs; and d) for remaining satellite minutia points, performing steps a) to c) with respective satellite minutia point assigned as planet minutia point to form a supercluster by iterating steps a) to d) and superadding the clusters; calculating a score of the supercluster based on the aggregate counted links; and comparing the score with a threshold. A biometric matching apparatus, a portable data carrier, a data processing unit comprising a matching apparatus and a computer program for implementing the invention are also disclosed.

Method and device for fall prevention and detection, specifically for the elderly care based on digital image analysis using an intelligent optical sensor. The fall detection is divided into two main steps; finding the person on the floor, and examining the way in which the person ended up on the floor. When the first step indicates that the person is on the floor, data for a time period of a few seconds before and after the indication is analyzed in the second step. If this indicates a fall, a countdown state is initiated in order to reduce the risk of false alarms, before sending an alarm. The fall prevention is also divided into two main steps: identifying a person entering a bed; and identifying the person leaving the bed to end up standing beside it.

Method and device for fall prevention and detection

Method and device for fall prevention and detection, specially for the elderly care based on digital image analysis using an intelligent optical sensor. The fall detection is divided into two main steps; finding the person on the floor, and examining the way in which the person ended up on the floor. The first step is further divided into algorithms investigating the percentage share of the body on the floor, the inclination of the body and the apparent length of the person. The second step includes algorithms examining the velocity and acceleration of the person. When the first step indicates that the person is on the floor, data for a time period of a few seconds before and after the indication is analysed in the second step. If this indicates fall, a countdown state is initiated in order to reduce the risk of false alarms, before sending an alarm. The fall prevention is also divided into two main steps; identifying a person entering a bed, and identifying the person leaving the bed to end up standing beside it. The second step is again further divided into algorithms investigating the surface area of on or more objects in an image, the inclination and the apparent length of these objects. When the second step indicates that a person is in an upright condition, a countdown state is initiated in order to allow for the person to return to the bed.

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Malmö, Sweden

Fredrik Rosqvist